Exploiting Coplanar Clusters to Enhance 3D Localization in Wireless Sensor Networks

TL;DR

This paper introduces a coplanarity-aware localization algorithm for 3D wireless sensor networks that improves accuracy by leveraging planar cluster information, addressing ambiguities caused by coplanar deployments.

Contribution

It proposes the Coplanarity Based Localization (CBL) algorithm that extends existing methods by incorporating coplanar cluster detection and 2D localization within clusters before 3D positioning.

Findings

Exploiting clustering improves localization precision.

The CBL algorithm reduces flip errors caused by coplanarity.

Clustering information enhances the accuracy of trilateration and quadrilateration.

Abstract

This thesis studies range-based WSN localization problem in 3D environments that induce coplanarity. In most real-world applications, even though the environment is 3D, the grounded sensor nodes are usually deployed on 2D planar surfaces. Examples of these surfaces include structures seen in both indoor (e.g. floors, doors, walls, tables etc.) and outdoor (e.g. mountains, valleys, hills etc.) environments. In such environments, sensor nodes typically appear as coplanar node clusters. We refer to this type of a deployment as a planar deployment. When there is a planar deployment, the coplanarity causes difficulties to the traditional range-based multilateration algorithms because a node cannot be unambiguously localized if the distance measurements to that node are from coplanar nodes. Thus, many already localized groups of nodes are rendered ineffective in the process just because they are coplanar. We, therefore propose an algorithm called Coplanarity Based Localization (CBL) that can be used as an extension of any localization algorithm to avoid most flips caused by coplanarity. CBL first performs a 2D localization among the nodes that are clustered on the same surface, and then finds the positions of these clusters in 3D. We have carried out experiments using trilateration for 2D localization, and quadrilateration for 3D localization, and experimentally verified that exploiting the clustering information leads to a more precise localization than mere quadrilateration. We also propose a heuristic to extract the clustering information in case it is not available, which is yet to be improved in the future.